Hot-pressing monitoring method and device for multilayer circuit board

Abstract

The invention relates to the technical field of circuit board hot pressing, and discloses a hot pressing monitoring method and device for a multi-layer circuit board. The method comprises the following steps: acquiring first verification data of the circuit board; performing data preprocessing according to the first verification data to obtain second verification data; performing data matching on the material component data based on a pre-established component database to obtain an optimal hot pressing parameter; when the material curvature data exceeds a preset flatness threshold value, performing adjustment value calculation based on a pre-established pressure parameter-curvature correlation model and a correlation degree matrix to obtain a pressure adjustment value; and inputting the optimal hot-pressing parameters and the pressure adjustment value into hot-pressing equipment, and performing monitoring and parameter adjustment on the hot-pressing equipment so as to enable the hot-pressing equipment to perform hot-pressing operation. According to the method, the curvature of the circuit board can be controlled while the hot-pressing temperature and humidity are controlled, so that the stability of the hot-pressing process and the mildew-proof performance of the circuit board are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hot pressing of circuit boards, and particularly to a method and device for monitoring the hot pressing of multi-layer circuit boards. Background Art

[0002] The hot pressing of multi-layer circuit boards is a manufacturing process that combines multi-layer circuit board materials into a whole through heating and pressing. In this process, each circuit layer (such as copper foil layer and insulating layer) is stacked together according to the design requirements, and is heated to a certain temperature by a hot press to soften the materials and promote the curing of the resin. At the same time, pressure is applied to tightly bond each layer. Finally, after cooling and curing, it is ensured that each layer is firmly connected to form a complete multi-layer circuit board.

[0003] During the hot pressing process of multi-layer circuit boards, it is necessary to precisely control the temperature and humidity parameters of the hot pressing equipment to ensure that the anti-mold materials can be evenly distributed and effectively cured. If the hot pressing temperature is too high, the anti-mold materials will be over-cured, resulting in the circuit board becoming brittle; while if the hot pressing time and temperature are too low, the anti-mold materials will not be fully cured, affecting their anti-mold effect. At the same time, the pressure during the hot pressing process also needs to be precisely controlled. Excessive pressure will cause the circuit board to deform, while too little pressure will not allow the anti-mold materials to fully contact the circuit board, affecting their curing quality. However, the existing hot pressing technology has problems in the inaccurate control of temperature, humidity and pressure, which easily leads to uneven distribution of anti-mold materials, thus affecting the curvature and anti-mold effect of the circuit board.

[0004] In summary, the existing hot pressing technology has problems in the inaccurate control of parameters such as temperature, humidity and pressure, which in turn reduces the stability of the hot pressing process and the anti-mold performance of the circuit board. Summary of the Invention

[0005] The present invention provides a method and device for monitoring the hot pressing of multi-layer circuit boards, which can control the curvature of the circuit board while controlling the hot pressing temperature and humidity, thereby improving the stability of the hot pressing process and the anti-mold performance of the circuit board.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a method for monitoring the hot pressing of multi-layer circuit boards, including: Obtaining first calibration data of the circuit board; wherein, the first calibration data includes material composition data and material curvature data; Performing data preprocessing on the first calibration data to obtain second calibration data; Performing data matching on the material composition data based on a pre-established composition database to obtain optimal hot pressing parameters; wherein, the composition database pre-stores the composition data of each material, and the optimal hot pressing parameters include hot pressing temperature and hot pressing humidity; Determine whether the material curvature data exceeds a preset flatness threshold. When the material curvature data exceeds the preset flatness threshold, calculate an adjustment value based on a pre-established pressure parameter-curvature correlation model and a correlation matrix to obtain a pressure adjustment value; wherein, the pressure parameter-curvature correlation model is pre-trained by a support vector machine. Input the optimal hot pressing parameters and the pressure adjustment value into the hot pressing equipment, and monitor and adjust the parameters of the hot pressing equipment so that the hot pressing equipment performs a hot pressing operation.

[0007] Preferably, the data matching of the material composition data based on a pre-established composition database to obtain the optimal hot pressing parameters includes: Calculate the material similarity based on the cosine similarity according to the material composition data and the composition data of each material in the composition database to obtain the material similarity. When the material similarity is greater than a preset material similarity threshold, determine that the material corresponding to the material composition data matches the corresponding material in the composition database successfully to obtain a target material; wherein, the target material is the material corresponding to the successful match in the composition database. Query the parameters based on the target material from a hot pressing parameter database to obtain the optimal hot pressing parameters; wherein, a mapping relationship is pre-established between the target material and the hot pressing parameters in the hot pressing parameter database.

[0008] Preferably, the calculation of the adjustment value based on a pre-established pressure parameter-curvature correlation model and a correlation matrix to obtain a pressure adjustment value includes: Input the material curvature data into a pre-established pressure parameter-curvature correlation model to obtain a pressure parameter. Based on the material curvature data and the pressure parameter, perform correlation matching based on the correlation matrix to obtain a curvature-pressure correlation degree. Calculate the adjustment value according to the curvature-pressure correlation degree to obtain a pressure adjustment value.

[0009] Preferably, the correlation matrix is constructed based on historical data, wherein each element in the correlation matrix represents the correlation degree between the material curvature data and the pressure parameter.

[0010] Preferably, the calculation of the adjustment value based on a pre-established pressure parameter-curvature correlation model and a correlation matrix to obtain a pressure adjustment value includes: Calculate the pressure adjustment value through the following formula:

[0011] In the formula, is the pressure adjustment value; is the correlation degree between the material curvature data and the pressure parameter; is the th pressure parameter, indicating the total number of pressure parameters.

[0012] Preferably, after calculating the adjustment value according to the curvature-pressure correlation degree to obtain the pressure adjustment value, the method further includes: adding the pressure adjustment value and the pressure parameter to obtain a pressure correction value; updating the pressure parameter-curvature correlation model based on the support vector machine according to the pressure correction value and the material curvature data.

[0013] Preferably, the monitoring and parameter adjustment of the hot pressing equipment includes: acquiring the real-time parameter data of the hot pressing equipment; performing parameter adjustment based on the fuzzy control algorithm and the PID algorithm according to the real-time parameter data and the optimal hot pressing parameters.

[0014] In a second aspect, the present invention provides a hot pressing monitoring device for a multi-layer circuit board, including: a data acquisition module, configured to acquire the first verification data of the circuit board; wherein, the first verification data includes material composition data and material curvature data; a data processing module, configured to perform data preprocessing according to the first verification data to obtain second verification data; a data matching module, configured to perform data matching on the material composition data based on a pre-established composition database to obtain the optimal hot pressing parameters; wherein, the composition database stores the composition data of each material in advance; an adjustment calculation module, configured to determine whether the material curvature data exceeds a preset flatness threshold, and when the material curvature data exceeds the preset flatness threshold, calculate an adjustment value based on a pre-established pressure parameter-curvature correlation model and a correlation matrix to obtain a pressure adjustment value; wherein, the pressure parameter-curvature correlation model is pre-trained by a support vector machine; a parameter input module, configured to input the optimal hot pressing parameters and the pressure adjustment value into the hot pressing equipment, and monitor and adjust the parameters of the hot pressing equipment so that the hot pressing equipment performs a hot pressing operation.

[0015] In a third aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the hot pressing monitoring method for a multi-layer circuit board described in any one of the above is implemented.

[0016] Fourthly, the present invention also provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the hot pressing monitoring method of the multi-layer circuit board described in any one of the above.

[0017] Compared with the prior art, the present invention has the following beneficial effects: (1) By preprocessing the component data of the circuit board material and performing data matching with a pre-established component database based on the cosine similarity matching technology, the present invention obtains the optimal hot pressing parameters that are most suitable for the material. Compared with the method of setting parameters relying on experience, this method can more accurately select appropriate hot pressing temperature and humidity according to the material characteristics, reduce parameter errors in the hot pressing process, and improve production consistency and product quality.

[0018] (2) When judging whether the material curvature data exceeds the preset flatness threshold, the present invention calculates the pressure adjustment value based on the pressure parameter-curvature correlation model and the correlation matrix trained by the support vector machine. Through learning historical data and dynamic adjustment, this method can accurately calculate the pressure correction value according to the curvature change of the circuit board, make the pressure applied in the hot pressing process more adaptable to the material characteristics, thereby effectively reducing the deformation of the circuit board and improving the flatness and reliability of the product.

[0019] (3) Based on real-time monitoring of the operating parameters of the hot pressing equipment, the present invention uses the fuzzy control algorithm and the PID algorithm for dynamic parameter adjustment. The fuzzy control can make flexible adjustments when the parameters fluctuate, while the PID algorithm ensures that the parameters converge to the optimal range. The combination of the two can accurately control the temperature, humidity and pressure of the hot pressing equipment, reduce the influence caused by environmental factors or equipment fluctuations, improve the stability of the hot pressing process, and reduce the defective rate of products.

[0020] (4) Based on multi-layer optimization methods such as data preprocessing, similarity matching, pressure adjustment and intelligent control, the present invention can reduce the test adjustment time and improve the degree of production automation. Compared with the method of adjusting hot pressing parameters relying on manual experience, this solution not only reduces manual intervention, improves production efficiency, but also reduces energy consumption and material loss. Description of the Drawings

[0021] Figure 1 is a schematic flowchart of the hot pressing monitoring method of the multi-layer circuit board provided by the first embodiment of the present invention; Figure 2 is a schematic structural diagram of the hot pressing monitoring device of the multi-layer circuit board provided by the second embodiment of the present invention. Detailed Embodiments

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Referring Figure 1 , the first embodiment of the present invention provides a hot pressing monitoring method for a multi-layer circuit board, including the following steps: S11, obtaining the first verification data of the circuit board.

[0024] S12, performing data preprocessing according to the first verification data to obtain second verification data.

[0025] S13, performing data matching on the material composition data based on a pre-established composition database to obtain the optimal hot pressing parameters.

[0026] S14, determining whether the material curvature data exceeds a preset flatness threshold. When the material curvature data exceeds the preset flatness threshold, calculating an adjustment value based on a pre-established pressure parameter-curvature correlation model and a correlation matrix to obtain a pressure adjustment value.

[0027] S15, inputting the optimal hot pressing parameters and the pressure adjustment value into a hot pressing device, and monitoring and adjusting the parameters of the hot pressing device so that the hot pressing device performs a hot pressing operation.

[0028] It should be noted that the hot pressing of a multi-layer circuit board is a manufacturing process that combines multi-layer circuit board materials into a whole through heating and pressing. In this process, each circuit layer (such as a copper foil layer and an insulating layer) is stacked together according to the design requirements, and is heated to a certain temperature by a hot press to soften the material and promote the curing of the resin. At the same time, pressure is applied to tightly bond each layer. Finally, after cooling and curing, it is ensured that each layer is firmly connected to form a complete multi-layer circuit board.

[0029] For the convenience of understanding the present invention, the following will further describe some preferred embodiments of the present invention.

[0030] In step S11, the first verification data of the circuit board is obtained.

[0031] Preferably, the first verification data includes material composition data and material curvature data.

[0032] Specifically, the material composition data includes the chemical composition or physical property data of the materials used in the multilayer circuit board. These data include the composition of the materials for each layer of the circuit board, such as the proportions and properties of copper, resin, ceramic, etc. These components will affect the reactions of the circuit board during the hot pressing process, such as the softening point, curing temperature, and coefficient of thermal expansion of the materials.

[0033] In an implementable manner, the acquisition of the material composition data can be through chemical analysis and physical experiment methods. For example, techniques such as X-ray fluorescence (XRF) analysis, energy-dispersive X-ray spectroscopy (EDS) analysis, and spectroscopic analysis can be used to accurately determine the composition of the materials, to understand the elemental composition of the materials, and to match the most suitable hot pressing parameters according to the data in subsequent steps. By processing and analyzing the composition data, a temperature range and humidity value can be set for each material to ensure stability and quality during the hot pressing process.

[0034] Specifically, the material curvature data refers to the deformation curvature of the circuit board during the hot pressing process. Since the multilayer circuit board is composed of different material layers, during hot pressing, the different thermal expansion characteristics of the materials will cause the circuit board to bend or deform. The material curvature data describes the degree of this deformation and can be obtained by measuring the bending degree of the circuit board surface at different points. In an achievable manner, the method for obtaining the material curvature data can utilize dedicated equipment, such as a laser scanner or a curvature measuring instrument, to measure the curvature change of the board surface. Through precise optical or mechanical methods, the deformation conditions of the board under different pressure and temperature conditions can be obtained.

[0035] It should be noted that the first verification data includes material composition data and material curvature data. Before processing these data, a series of verification steps must be carried out to exclude outliers and ensure the accuracy of subsequent data analysis and hot pressing parameter adjustment. In an optional implementation, appropriate thresholds can be set based on the statistical analysis of historical data to determine whether the data is normal. In actual operation, the verification steps for each type of data involve presetting an initial outlier judgment threshold. For different types of data, such as material composition data and curvature data, there will be different judgment criteria and methods. In these steps, first, a historical data set of the same data type as the current data is extracted from the historical database. These historical data sets contain a large amount of data from the actual production process, which can provide a statistical basis for the verification of the current data. After extracting the historical data, the mean and standard deviation of the data set are calculated. The mean and standard deviation are the basic statistical characteristics of the data distribution. The mean represents the central tendency of the historical data, and the standard deviation reflects the range of data fluctuations. Based on the mean and standard deviation, the normal fluctuation range of the current data can be obtained, and then a reasonable threshold can be calculated. When the value of the data exceeds the adjusted threshold range, the data is marked as an outlier and removed. This process is achieved by comparing with the threshold. If a data point deviates significantly from the normal range, it is determined to be abnormal and does not participate in subsequent processing and analysis. By removing these outliers, the remaining data will be more accurate, ensuring that the adjustment parameters of the subsequent hot pressing equipment will not be disturbed.

[0036] In step S12, according to the first verification data, data preprocessing is performed to obtain second verification data.

[0037] Specifically, the first step of data preprocessing is to clean the original first verification data. When obtaining the original data, it may be affected by factors such as equipment accuracy, measurement environment, and external interference, resulting in noise or error values in the data. These noises will affect the accuracy of subsequent analysis and calculations, so the data needs to be cleaned. Exemplarily, the cleaning process includes removing null values, duplicate data, and detected abnormal data. For missing or incomplete data, interpolation methods, mean filling, or other appropriate methods can be used to fill them; for outliers, unreasonable data can be removed through outlier detection methods, such as the threshold judgment mentioned above. Through this data cleaning, the data for subsequent processing is ensured to be cleaner and more complete.

[0038] Specifically, the second step of data preprocessing is to standardize and normalize the data. In the original first verification data, the dimensions and value ranges of the material composition data and the curvature data may vary. Direct calculation and analysis may cause the influence of certain features on the final result to be too large or too small. To eliminate this influence, it is necessary to standardize or normalize the data. The standardization process is to convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1, which can ensure the comparability between different features. Normalization is to compress the value range of the data into a specific interval (such as [0, 1]) to avoid the unbalanced influence of some data on the analysis result due to too large or too small values. These two processing methods can effectively improve the accuracy of data processing and ensure that the contributions of different features to the analysis result are more balanced.

[0039] Specifically, the third step of data preprocessing is to smooth the data to reduce the fluctuations caused by the accuracy of the measurement device or environmental factors. Exemplarily, smoothing algorithms (such as moving average, weighted average or Gaussian smoothing) can be used to reduce the high-frequency noise in the data, making the trend of the data more obvious and easier to identify. Smoothing processing can help remove some short-term fluctuations or accidental interferences, making the data more stable and reliable as a whole.

[0040] In step S13, data matching is performed on the material composition data based on a pre-established composition database, and the optimal hot pressing parameters are obtained. The composition database pre-stores the composition data of each material; wherein, the optimal hot pressing parameters include the hot pressing temperature and the hot pressing humidity.

[0041] Preferably, the data matching of the material composition data based on the pre-established composition database to obtain the optimal hot pressing parameters includes: Based on the material composition data and the composition data of each material in the composition database, calculate the similarity based on the cosine similarity to obtain the material similarity; When the material similarity is greater than a preset material similarity threshold, it is determined that the material corresponding to the material composition data matches the corresponding material in the composition database successfully, and the target material is obtained; wherein, the target material is the material corresponding to the successful match in the composition database; According to the target material, query the parameters based on the hot pressing parameter database to obtain the optimal hot pressing parameters; wherein, a mapping relationship is pre-established between the target material and the hot pressing parameters in the hot pressing parameter database.

[0042] Specifically, the material composition data contains the specific composition information of the materials used in the multi-layer circuit board. This information usually includes data such as the chemical element content and molecular composition of different materials. During the manufacturing process, the composition of the material directly affects its behavior during the hot pressing process, such as its expansibility, curing characteristics, and thermal conductivity. To achieve precise hot pressing control, it is necessary to select suitable hot pressing temperature and humidity according to the composition of the material. The composition database stores the composition data of various materials, and there is a mapping relationship between these data and the corresponding hot pressing parameters. By querying the hot pressing parameter database, the optimal hot pressing parameters can be obtained based on the composition characteristics of the material to ensure the best effect during the hot pressing process.

[0043] Specifically, the similarity between the material composition data and the composition data of each material in the composition database is calculated to determine the type of material. Preferably, the cosine similarity calculation is used for the similarity calculation. Cosine similarity measures the similarity by calculating the angle between two data vectors. Its basic idea is to judge the matching degree between the input material composition data and the stored material composition data in the database by measuring the similarity. Specifically, the calculation formula of cosine similarity is based on the dot product between two vectors divided by their respective norms. The obtained similarity value can be used to quantify the similarity between material compositions. When the similarity value is high, it indicates that the compositions of the two materials are very similar; otherwise, it means that their compositions are quite different. Therefore, after calculating the similarity, it is necessary to make a judgment according to the preset material similarity threshold. If the calculated similarity is greater than the preset similarity threshold, the system will determine that the input material composition data matches the composition data of a certain type of material in the database and determine the target material. This judgment process ensures the accurate identification of the material characteristics during the hot pressing process, so that the most suitable hot pressing parameters for the material can be selected. It should be noted that the target material refers to a certain type of material in the database that is most similar to the input material composition. Such materials will have similar hot pressing requirements, so they can provide appropriate parameters for subsequent hot pressing operations.

[0044] Specifically, according to the target material, query through the hot pressing parameter database to obtain the optimal hot pressing parameters corresponding to the target material. The hot pressing parameter database pre-stores the hot pressing parameters related to various materials. Optionally, these hot pressing parameters can be obtained through a large amount of experimental data, production experience, and theoretical analysis. A mapping relationship is pre-established between the target material and the hot pressing parameters in the parameter database. These parameters include hot pressing temperature and humidity, which directly affect the quality of the multi-layer circuit board. For example, some materials may require higher temperature and humidity to achieve better curing effects, while other materials may achieve ideal effects at lower temperature and humidity. By querying parameters based on the target material, the system can provide accurate hot pressing parameters for the current material, ensuring precise control of key factors such as temperature and humidity during the production process, thereby improving the product quality and production efficiency.

[0045] In step S14, it is determined whether the material curvature data exceeds a preset flatness threshold. When the material curvature data exceeds the preset flatness threshold, a pressure adjustment value is calculated based on a pre-established pressure parameter-curvature correlation model and a correlation matrix; wherein, the pressure parameter-curvature correlation model is pre-trained by a support vector machine.

[0046] Preferably, the calculating the pressure adjustment value based on the pre-established pressure parameter-curvature correlation model and the correlation matrix includes: Input the material curvature data into the pre-established pressure parameter-curvature correlation model to obtain pressure parameters; Based on the material curvature data and the pressure parameters, perform correlation matching based on the correlation matrix to obtain the curvature-pressure correlation degree; Calculate the adjustment value according to the curvature-pressure correlation degree to obtain the pressure adjustment value.

[0047] Preferably, the correlation matrix is constructed based on historical data, where each element in the correlation matrix represents the correlation degree between the material curvature data and the pressure parameters.

[0048] It should be noted that determining whether the material curvature data exceeds the preset flatness threshold involves monitoring and adjusting the possible deformation of the circuit board during the hot pressing process. Specifically, the material curvature data describes the deformation situation of the circuit board during the hot pressing process, and the curvature value reflects the degree of bending of the circuit board surface. If the material curvature data exceeds the preset flatness threshold, it indicates that the deformation of the circuit board exceeds the allowable range, which may affect the quality and performance of the final product. Therefore, it is very necessary to make timely adjustments.

[0049] Specifically, the material curvature data is input into a pre-established pressure parameter-curvature correlation model. It should be noted that the pressure parameter-curvature correlation model is trained by the Support Vector Machine (SVM) algorithm. The support vector machine is a machine learning method commonly used for classification and regression, which can divide data into different categories by finding the optimal hyperplane or decision boundary. Optionally, in the present invention, the support vector machine is trained with historical data to obtain a correlation model that can predict the pressure parameter based on the material curvature. The purpose of this model is to predict the pressure parameter that matches the given curvature data. The pressure parameters include the pressure values that need to be applied during the hot pressing process, and these values directly determine the compressive force applied to the board during the hot pressing process, thereby affecting its final shape and quality.

[0050] Specifically, according to the material curvature data and the pressure parameters obtained through the correlation model, the correlation degree matrix is used for correlation degree matching to obtain the correlation degree of curvature-pressure. Preferably, the correlation degree matrix is constructed based on historical data, which contains the mutual relationship between the material curvature data and the pressure parameters. When constructing the correlation degree matrix, the mathematical relationship between the curvature and the pressure parameters of different materials in the historical data is analyzed, and each matrix element represents the correlation degree between the curvature data and the pressure parameters, which can be calculated. Exemplarily, the calculation method of the correlation degree can be calculated using the Pearson Correlation Coefficient or covariance, and the cosine similarity mentioned above can also be used. It should be noted that the Pearson correlation coefficient formula can be expressed as: , where and represent the values of two sets of data, and represent the means of two sets of data, is the Pearson correlation coefficient.

[0051] Specifically, based on the obtained curvature-pressure correlation degree, the adjustment value is calculated to obtain the pressure adjustment value. The purpose of this step is to adjust the pressure applied during the hot pressing process according to the change of the material curvature. Through this adjustment, the system can ensure that the appropriate pressure is applied to the circuit board during the hot pressing process, thereby avoiding deformation and ensuring the quality of the product.

[0052] Preferably, the calculation of the pressure adjustment value based on the pre-established pressure parameter-curvature correlation model and the correlation degree matrix to obtain the pressure adjustment value includes: The pressure adjustment value is calculated by the following formula:

[0053] In the formula, is the pressure adjustment value; is the correlation degree between the material curvature data and the pressure parameters; is the th pressure parameter, indicating the total number of pressure parameters.

[0054] It should be noted that after the pressure adjustment value is input into the hot pressing equipment in the subsequent steps, it needs to be superimposed or corrected with the original pressure parameters to form a new pressure value to guide the actual hot pressing process.

[0055] Preferably, after calculating the adjustment value according to the curvature-pressure correlation degree to obtain the pressure adjustment value, it further includes: Adding the pressure adjustment value and the pressure parameter to obtain a pressure correction value; Updating the pressure parameter-curvature correlation model based on the support vector machine according to the pressure correction value and the material curvature data.

[0056] Specifically, in the control system of the hot pressing process, the magnitude of the pressure directly affects the forming effect and the final quality of the material. When the system calculates the pressure adjustment value according to the material curvature data, this adjustment value reflects the influence of the current deformation of the material (i.e., curvature change) on the pressure required in the hot pressing process. By adding the calculated pressure adjustment value to the originally set pressure parameters to obtain a new pressure correction value, this step is to optimize the hot pressing process in real time. As mentioned in the above steps, the pressure parameters are preset, and a series of pressure values are set before hot pressing. However, due to the differences in materials during the production process, especially the curvature of different materials may change, resulting in insufficient or excessive preset pressure parameters. Therefore, the addition of the pressure adjustment value is to dynamically adjust the pressure value according to real-time data, so as to ensure that the pressure applied during the hot pressing process adapts to the change of the material curvature.

[0057] Specifically, after calculating the pressure correction value, the system needs to further adjust the pressure parameter - curvature correlation model to ensure that during the subsequent hot pressing process, the relationship between pressure and material can be more accurate and real-time. Support Vector Machine (SVM) is a supervised learning method commonly used in regression and classification problems. In this step, the task of SVM is to learn and update the pressure parameter - curvature correlation model based on the existing material curvature data and the pressure correction value. This model is essentially a mapping function that can predict the pressure parameter to be applied based on the input material curvature data. By learning from historical data, SVM can identify the relationship between material curvature changes and pressure requirements, and through continuous updates, enable the model to make more accurate predictions when facing new data. Specifically, the system takes the material curvature data and the calculated pressure correction value as inputs for training. During the training process, SVM finds an optimal hyperplane (or decision boundary in a multi-dimensional space) to separate and fit the data, thereby finding the best mapping relationship between material curvature and pressure. Through this training, SVM can understand and capture the influence pattern of curvature changes on pressure adjustment, and use this model to guide pressure adjustment during the subsequent hot pressing process.

[0058] In step S15, input the optimal hot pressing parameters and the pressure adjustment value into the hot pressing equipment, and monitor and adjust the parameters of the hot pressing equipment to enable the hot pressing equipment to perform a hot pressing operation.

[0059] Preferably, the monitoring and parameter adjustment of the hot pressing equipment includes: Obtain the real-time parameter data of the hot pressing equipment; Based on the real-time parameter data and the optimal hot pressing parameters, perform parameter adjustment based on the fuzzy control algorithm and the PID algorithm.

[0060] Specifically, the real-time parameter data of the hot pressing equipment refers to the key working state information of temperature, pressure, and humidity collected in real-time by equipment sensors during the hot pressing process. These data reflect the operating state of the equipment at the current moment, such as the current temperature of the hot press, the applied pressure, and the humidity factor inside the equipment. Since the operation of the equipment may be affected by factors such as environmental changes, equipment performance, and raw material differences, the real-time parameters may deviate from the set optimal hot pressing parameters. Therefore, real-time monitoring of various parameters of the equipment is a prerequisite for ensuring the stable progress of the hot pressing process. By collecting these real-time data, the system can evaluate the current working state of the equipment in real-time, compare these data with the preset optimal hot pressing parameters, and analyze the gap. This gap is the amount that needs to be adjusted, that is, the error between the target parameter and the actual parameter. The system will determine how to adjust the various parameters of the equipment based on these errors to keep the equipment in the best working state for hot pressing.

[0061] Specifically, after obtaining the real-time parameter data, the system needs to use the fuzzy control algorithm and the PID algorithm to adjust the equipment. The goal is to adjust the actual parameters of the equipment to a state as close as possible to the optimal hot pressing parameters, so as to ensure the precise control of the hot pressing process. The fuzzy control and the PID algorithm have different control strategies, and the combined use can make up for the limitations of a single algorithm and provide more precise and flexible adjustment.

[0062] It is worth noting that fuzzy control is a control method suitable for complex and uncertain systems. Different from traditional control methods, the fuzzy control algorithm does not require an accurate mathematical model, but processes the input data through fuzzyfication and controls the system based on a series of fuzzy rules. During the hot pressing process, real-time parameters (such as temperature, pressure, etc.) may fluctuate due to various factors, and the fuzzy control algorithm can flexibly handle these fluctuations. The fuzzy control algorithm first converts the differences between the real-time parameter data and the optimal hot pressing parameters into fuzzy quantities. For example, if the temperature is too low, it is inferred according to the fuzzy rules that "the temperature needs to be increased", and a control quantity is further generated to guide how the system should be adjusted. By setting a series of fuzzy rules (for example, "if the temperature is too low, increase the pressure"), the fuzzy control can provide reasonable control instructions under uncertain circumstances. Through the fuzzy control system, the real-time parameters can be flexibly adjusted to be as close as possible to the optimal hot pressing parameters to ensure the stable operation of the equipment.

[0063] It is worth noting that the PID (Proportional-Integral-Derivative) control algorithm is a classic feedback control method widely used in industrial automation systems. The goal of the PID algorithm is to minimize the error in the control system and adjust the actual output of the system (such as temperature, pressure, etc.) to the expected value (i.e., the optimal hot pressing parameters). The PID algorithm adjusts according to the magnitude of the error, the accumulation of the error, and the rate of change of the error, so as to achieve precise control. The PID algorithm calculates the control quantity through three main parameters: proportional, integral, and derivative. The proportional term is adjusted according to the current error, the integral term is used to eliminate the error accumulated over a long period, and the derivative term makes a forward-looking adjustment by predicting the change trend of the error. During the hot pressing process, the gap between the real-time temperature, pressure, etc. and the target value will be input into the PID controller as the error. The PID controller quickly adjusts the equipment state to be close to the optimal hot pressing parameters through real-time adjustment.

[0064] In summary, the present invention provides a hot pressing monitoring method for a multi-layer circuit board, including: obtaining first verification data of the circuit board; wherein, the first verification data includes material composition data and material curvature data; performing data preprocessing according to the first verification data to obtain second verification data; performing data matching on the material composition data based on a pre-established composition database to obtain optimal hot pressing parameters, the composition database pre-storing the composition data of each material; wherein, the optimal hot pressing parameters include hot pressing temperature and hot pressing humidity; determining whether the material curvature data exceeds a preset flatness threshold, and when the material curvature data exceeds the preset flatness threshold, calculating an adjustment value based on a pre-established pressure parameter-curvature correlation model and a correlation matrix to obtain a pressure adjustment value; wherein, the pressure parameter-curvature correlation model is pre-trained by a support vector machine; inputting the optimal hot pressing parameters and the pressure adjustment value into a hot pressing device, and monitoring and adjusting the parameters of the hot pressing device so that the hot pressing device performs a hot pressing operation.

[0065] In the present invention, the method first obtains the first verification data of the circuit board and performs data preprocessing to obtain second verification data including material composition data and material curvature data; through component matching based on cosine similarity and in combination with a pre-established composition database, the optimal hot pressing parameters matching the material are obtained. The optimal hot pressing parameters are input into the hot pressing device. At the same time, the pressure parameters are also calculated and adjusted in real time through a pressure parameter-curvature correlation model and a correlation matrix to ensure the matching of pressure and curvature during the hot pressing process. When the material curvature data exceeds the preset flatness threshold, the calculation of the pressure adjustment value is further optimized by a support vector machine. In addition, the parameters input into the hot pressing device are monitored and adjusted in real time through a fuzzy control algorithm and a PID algorithm to ensure the precise control of each parameter. Through the above steps, the present invention can control the curvature of the circuit board while controlling the hot pressing temperature and humidity, thereby improving the stability of the hot pressing process and the mildew-proof performance of the circuit board.

[0066] Referring to Figure 2 , the second embodiment of the present invention provides a hot pressing monitoring device for a multi-layer circuit board, including: A data acquisition module, configured to acquire first verification data of the circuit board; wherein, the first verification data includes material composition data and material curvature data; A data processing module, configured to perform data preprocessing according to the first verification data to obtain second verification data; A data matching module, configured to perform data matching on the material composition data based on a pre-established composition database to obtain optimal hot pressing parameters; wherein, the composition database pre-stores the composition data of each material; An adjustment calculation module is configured to determine whether the material curvature data exceeds a preset flatness threshold. When the material curvature data exceeds the preset flatness threshold, an adjustment value is calculated based on a pre-established pressure parameter-curvature correlation model and a correlation matrix to obtain a pressure adjustment value. Wherein, the pressure parameter-curvature correlation model is pre-trained by a support vector machine. A parameter input module is configured to input the optimal hot pressing parameters and the pressure adjustment value into a hot pressing device, and monitor and adjust the parameters of the hot pressing device, so that the hot pressing device performs a hot pressing operation.

[0067] It should be noted that the hot pressing monitoring device for a multi-layer circuit board provided in an embodiment of the present invention is used to execute all the process steps of the hot pressing monitoring method for a multi-layer circuit board in the above embodiment. The working principles and beneficial effects of the two correspond one by one, and thus will not be elaborated herein.

[0068] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a hot pressing monitoring method program for a multi-layer circuit board. When the processor executes the computer program, the steps in the above embodiments of the hot pressing monitoring method for a multi-layer circuit board are implemented, such as Figure 1 the step S11 shown. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiments are implemented, such as a data matching module.

[0069] Exemplarily, the computer program may be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0070] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device, and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above, or combine some components, or different components. For example, the electronic device may further include input / output devices, network access devices, a bus, etc.

[0071] The so-called processor may be a Central Processing Unit (CPU), or it may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device through various interfaces and circuits.

[0072] The memory can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory, the processor realizes various functions of the electronic device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0073] Among them, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0074] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative work.

[0075] The above-described specific embodiments have further elaborated on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring thermal pressure of a multilayer circuit board, characterized in that: include: Acquire first verification data of the circuit board; wherein the first verification data includes material composition data and material curvature data; Performing data preprocessing according to the first verification data to obtain second verification data; The material composition data is matched based on a pre-established composition database to obtain optimal hot pressing parameters; wherein the composition database pre-stores the composition data of each material, and the optimal hot pressing parameters include hot pressing temperature and hot pressing humidity; Determine whether the material curvature data exceeds a preset flatness threshold, and when the material curvature data exceeds the preset flatness threshold, calculate an adjustment value based on a pre-established pressure parameter-curvature association model and an association matrix to obtain a pressure adjustment value; wherein the pressure parameter-curvature association model is pre-trained by a support vector machine; The optimal hot pressing parameters and the pressure adjustment value are input into the hot pressing equipment, and the hot pressing equipment is monitored and the parameters are adjusted so that the hot pressing equipment performs hot pressing operation.

2. The method for monitoring the thermal pressure of a multilayer circuit board according to claim 1, characterized in that: The data matching of the material composition data based on the pre-established composition database to obtain the optimal hot pressing parameters includes: According to the material composition data and the composition data of each material in the composition database, similarity calculation is performed based on cosine similarity to obtain material similarity; When the material similarity is greater than a preset material similarity threshold, it is determined that the material corresponding to the material composition data successfully matches the corresponding material in the composition database, and a target material is obtained; wherein the target material is the corresponding successfully matched material in the composition database; According to the target material, a parameter query is performed based on a hot pressing parameter database to obtain optimal hot pressing parameters; wherein a mapping relationship is pre-established between the target material and the hot pressing parameters in the hot pressing parameter database.

3. The method for monitoring the thermal pressure of a multilayer circuit board according to claim 1, characterized in that: The adjusting value calculation based on the pre-established pressure parameter-curvature correlation model and correlation matrix to obtain the pressure adjustment value includes: Inputting the material curvature data into a pre-established pressure parameter-curvature correlation model to obtain a pressure parameter; According to the material curvature data and the pressure parameter, correlation matching is performed based on a correlation matrix to obtain a curvature-pressure correlation; An adjustment value is calculated according to the curvature-pressure correlation to obtain a pressure adjustment value.

4. The method for monitoring the thermal pressure of a multilayer circuit board according to claim 3, characterized in that: The correlation matrix is ​​constructed based on historical data, wherein each element in the correlation matrix represents the correlation between material curvature data and pressure parameters.

5. The method for monitoring the thermal pressure of a multilayer circuit board according to claim 3, characterized in that: The step of calculating the adjustment value according to the curvature-pressure correlation to obtain the pressure adjustment value includes: Calculate the pressure adjustment value using the following formula: In the formula, is the pressure adjustment value; is the correlation between material curvature data and pressure parameters; For the Pressure parameters, Indicates the total number of pressure parameters.

6. The method for monitoring the thermal pressure of a multilayer circuit board according to claim 3, characterized in that: After calculating the adjustment value according to the curvature-pressure correlation to obtain the pressure adjustment value, the method further includes: Adding the pressure adjustment value and the pressure parameter to obtain a pressure correction value; The pressure parameter-curvature association model is updated based on a support vector machine according to the pressure correction value and the material curvature data.

7. The method for monitoring the thermal pressure of a multilayer circuit board according to claim 1, characterized in that: The monitoring and parameter adjustment of the hot pressing equipment includes: Obtain real-time parameter data of hot pressing equipment; According to the real-time parameter data and the optimal hot pressing parameters, parameter adjustment is performed based on a fuzzy control algorithm and a PID algorithm.

8. A thermal pressure monitoring device for a multi-layer circuit board, characterized in that: include: A data acquisition module, used to acquire first verification data of the circuit board; wherein the first verification data includes material composition data and material curvature data; A data processing module, used for performing data preprocessing according to the first verification data to obtain second verification data; A data matching module, used for matching the material composition data based on a pre-established composition database to obtain optimal hot pressing parameters; wherein the composition database pre-stores the composition data of each material; An adjustment calculation module, used to determine whether the material curvature data exceeds a preset flatness threshold, and when the material curvature data exceeds the preset flatness threshold, an adjustment value is calculated based on a pre-established pressure parameter-curvature association model and an association matrix to obtain a pressure adjustment value; wherein the pressure parameter-curvature association model is pre-trained by a support vector machine; The parameter input module is used to input the optimal hot pressing parameters and the pressure adjustment value into the hot pressing equipment, and monitor and adjust the parameters of the hot pressing equipment so that the hot pressing equipment performs hot pressing operation.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the thermal pressure monitoring method of a multilayer circuit board as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the thermal pressure monitoring method for a multilayer circuit board as described in any one of claims 1 to 7.

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